Mortality Risk Index Research Articles

Insulin Resistance–Nutritional Index: A Simple Index and Potential Predictor of Mortality Risk in Patients with Chronic Heart Failure and Type 2 Diabetes

Insulin Resistance–Nutritional Index: A Simple Index and Potential Predictor of Mortality Risk in Patients with Chronic Heart Failure and Type 2 Diabetes

Assessment of the Impact of Home-Based Hospitalization on Health Outcomes: An Observational Study.

In Portugal, evidence of clinical outcomes within home-based hospitalization programs remains limited. Despite the adoption of homebased hospitalization services, it is still unclear whether these services represent an effective way to manage patients compared with inpatient hospital care. Therefore, the aim of this study was to evaluate the outcomes of home-based hospitalization compared with conventional hospitalization in a group of patients with a primary diagnosis of infectious, cardiovascular, oncological, or 'other' diseases. An observational retrospective study using anonymized administrative data to investigate the outcomes of home-based hospitalization (n = 209) and conventional hospitalization (n = 192) for 401 Portuguese patients admitted to CUF hospitals (Tejo, Cascais, Sintra, Descobertas, and the Unidade de Hospitalização Domiciliária CUF Lisboa). Data on demographics and clinical outcomes, including Barthel index, Braden scale, Morse scale, mortality, and length of hospital stay, were collected. The statistical analysis included comparison tests and logistic regression. The study found no statistically significant differences between patients' admission and discharge for the Barthel index, Braden scale, and Morse scale scores, for both conventional and home-based hospitalizations. In addition, no statistically significant differences were found in the length of stay between conventional and home-based hospitalization, although patients diagnosed with infectious diseases had a longer stay than patients with other conditions. Although the mortality rate was higher in home-based hospitalization compared to conventional hospitalization, the mortality risk index (higher in home-based hospitalization) assessed at admission was a more important predictor of death than the type of hospitalization. The study found that there were no significant differences in outcomes between conventional and home-based hospitalization. Home-based hospitalization was found to be a valuable aspect of patient- and family-centered care. However, it is noteworthy that patients with infectious diseases experienced longer hospital stays.

Enhanced Drought Exposure Increasingly Threatens More Forests Than Observed

AbstractForest protection and afforestation have been identified as a means to partially offset anthropogenic CO2 emissions. Yet, increasingly frequent observations of drought‐induced tree mortality are reported. Here, we applied a risk analysis framework for global drought‐induced forest mortality by examining extreme reductions in greenness and water content of forest canopies during past mortality events as well as growth recovery of surviving individual trees following stand‐scale mortality events. We defined a drought‐induced mortality risk index (DMR) that explains 80% of documented tree mortality. Rising CO2 alleviated the increase of DMR with short‐term drought, however, the observed DMR increases with long‐term drought no matter whether considering plant responses to CO2. DMR in sites where tree mortality has been observed significantly increased since the 1980s. More than that, drought exposure threatened 0.28 billion hectares of forested areas. Our framework highlights how climate change‐induced drought, especially hotter‐droughts, threatens the sustainability of global forests.

Advanced lung cancer inflammation index combined with geriatric nutritional risk index predict all-cause mortality in heart failure patients

BackgroundThis study was undertaken to explore the predictive value of the advanced lung cancer inflammation index (ALI) combined with the geriatric nutritional risk index (GNRI) for all-cause mortality in patients with heart failure (HF).Methods and resultsWe enrolled 1123 patients with HF admitted to our cardiology department from January 2017 to October 2021. Patients were divided into four groups, according to the median ALI and GNRI. From the analysis of the relationship between the ALI and GNRI, we concluded that there was a mild positive linear correlation (r = 0.348, p < 0.001) and no interaction (p = 0.140) between the ALI and GNRI. Kaplan‒Meier analysis showed that the cumulative incidence of all-cause mortality in patients with HF was highest in Group 1 (log-rank χ2 126.244, p < 0.001). Multivariate Cox proportional hazards analysis revealed that ALI and GNRI were independent predictors of all-cause mortality in HF patients (ALI: HR 0.407, 95% CI 0.296–0.560, p < 0.001; GNRI: HR 0.967, 95% CI 0.954–0.980, p < 0.001). The area under the curve (AUC) for ALI combined with GNRI was 0.711 (p < 0.001), according to the time-dependent ROC curve.ConclusionALI and GNRI were independent predictors of all-cause mortality in HF patients. Patients with HF had the highest risk of all-cause mortality when the ALI was < 24.60 and the GNRI was < 94.41. ALI combined with the GNRI has good predictive value for the prognosis of HF patients.

Predictive Value of PRISM-4, PIM-3, CRP, Albumin, CRP/Albumin Ratio and Lactate in Critically Ill Children.

The accurate prediction of the prognosis for critically ill children is crucial, with the Pediatric Index of Mortality (PIM) and Pediatric Risk of Mortality (PRISM) being extensively utilized for this purpose. Albumin, C-reactive protein (CRP), and lactate levels, which are indicative of inflammation and circulatory status in critically ill children, have not been incorporated into existing scoring systems. This retrospective cohort study evaluated the association between biological markers and the clinical outcomes in children with critical illnesses. PRISM-4 and PIM-3 death probability (DP), albumin, lactate, CRP, and CRP/albumin ratio were recorded upon admission. The accuracy of the indexes in predicting mortality were assessed by calculating the area under the curve (AUC). There were 942 patients included and the 28-day mortality rate was 7.9%. The AUC for PRISM-4, PIM-3, CRP, CRP/albumin ratio, albumin, and lactate were 0.923, 0.896, 0.798, 0.795, 0.751, 0.728, respectively. The findings in the subgroup analysis of septic patients were similar to those found in the overall population. Although CRP, CRP/albumin ratio, albumin, and lactate levels are all linked to mortality in children, CRP and the CRP/albumin ratio have lower predictive values than albumin and lactate. Incorporation of albumin and lactate into scoring systems will improve predictability.

Neighborhood Disadvantage and Parenting in Early Adolescence Predict Epigenetic Aging and Mortality Risk in Adulthood.

Youth who grow up in disadvantaged neighborhoods experience poorer health later in life, but little is known about the biological mechanisms underlying these effects and socioenvironmental factors that may protect youth from the biological embedding of neighborhood adversity. This study tests whether supportive and consistent parenting buffers associations between neighborhood disadvantage in early adolescence and epigenetic aging in adulthood. A community sample from Birmingham, Alabama, USA (N = 343; 57% female; 81% Black, 19% White) was assessed in early adolescence (T1; ages 11 and 13) and adulthood (T2; age 27). At T1, neighborhood poverty was derived from census data and neighborhood disorder was reported by caregivers. Both youth and parents reported on parental discipline and nurturance. At T2, methylation of salivary DNA was used to derive a mortality risk index and Hannum, Horvath, PhenoAge, and GrimAge epigenetic age estimators. Regression analyses revealed that neighborhood disadvantage was associated with accelerated epigenetic aging and/or mortality risk only when combined with high levels of harsh and inconsistent discipline and low child-reported parental nurturance. These findings identify epigenetic aging and mortality risk as relevant mechanisms through which neighborhood adversity experienced in adolescence may affect later health; they also point to the importance of supportive and consistent parenting for reducing the biological embedding of neighborhood adversity in early adolescence.

Prognostic Value of Nutritional Markers for Long-Term Mortality in Patients Undergoing Endovascular Aortic Repair.

Objective: The relationship between nutritional status and morbidity and death in a number of diseases and disorders has garnered considerable attension. In patients having endovascular aneurysm repair (EVAR) for abdominal aortic aneurysms (AAA), we assessed the prognostic value of nutritional markers of albumin (ALB), body mass index (BMI), and geriatric nutritional risk index (GNRI) for long-term mortality. Materials and Methods: Retrospective data analysis was done on patients who had undergone elective EVAR for AAA more than 5 years earlier. Results: A total of 176 patients underwent EVAR for AAA between March 2012 and April 2016. The optimal cutoff value of ALB, BMI, and GNRI for predicting long-term mortality was calculated as 3.75 g/dL (area under the curve [AUC] 0.64), 21.4 kg/m2 (AUC 0.65), and 101.4 (AUC 0.70), respectively. Low ALB, low BMI, and low GNRI as well as age ≥75 years, chronic obstructive pulmonary disease, chronic kidney disease, and active cancer were independent risk factors for long-term mortality. Conclusion: Malnutrition, which is measured by ALB, BMI, and GNRI, is an independent risk factor for long-term mortality in patients receiving EVAR for AAA. Of the nutritional markers, the GNRI can be the most reliable nutritional indicator to identify a potentially high-risk group of mortality after EVAR.

Performance of Brazilian Pediatric Risk of Severity Model for Illness (Brprism) Compared to Pediatric Index of Mortality and Pediatric Risk of Mortality 2

Introduction: The best prognosis score with which to evaluate high-risk patients upon admission into pediatric intensive care is not well established in resource-limited settings. The objective of study was to formulate a risk-of-illness severity model for pediatric mortality to be applied upon PICU admission in resource-limited settings. Methods: Our study was designed to develop an illness severity index and a prognostic model for critically ill children. A prospective, observational multicenter pilot study, performed between February 1995 and October 1999, evaluated the variables, methodology and statistical techniques for the development of a model. A single-center prospective cohort study, performed between November 1999 and October 2004, collected information from consecutive admissions into the Pediatric Intensive Care Unit (PICU) at a high-complexity university, teaching, and reference hospital in São Paulo, Brazil. Results: In the pilot study, 1,459 patients (a PICU mortality rate of 16%) were included, and in the second study, 1,033 patients (a PICU mortality rate of 13.9% and a hospital mortality rate of 6.9% after PICU discharge) were included. We used multivariable regression to determine two probabilistic models; the first addressed survival and the overall probability of death (hospital plus PICU deaths), and the second was conditional (i.e., PICU death). An illness severity index stratified these probabilities into three risk strata: low-, medium- and high-risk patients. In the final step, the new death probabilities were estimated using a Bayesian adjustment. Conclusions: The model estimates three probabilities (survival, death in the PICU and death in the hospital after PICU discharge) stratified into three risk categories. To the best of our knowledge, this is the first study using a Bayesian adjustment to determine a prognosis and illness severity, and it should enable us to make therapeutic adjustments and provide appropriate counseling for high-risk patients in resource-limited settings.

Development and validation of a new prognostic index for mortality risk in multimorbid adults.

Multimorbidity is highly prevalent among older adults and associated with a high mortality. Prediction of mortality in multimorbid people would be clinically useful but there is no mortality risk index designed for this population. Our objective was therefore to develop and internally validate a 1-year mortality prognostic index for older multimorbid adults. We analysed data of the OPERAM cohort study in Bern, Switzerland, including 822 adults aged 70 years or more with multimorbidity (3 or more chronic medical conditions) and polypharmacy (use of 5 drugs or more for >30 days). Time to all-cause mortality was assessed up to 1 year of follow-up. We performed a parametric Weibull regression model with backward stepwise selection to identify mortality risk predictors. The model was internally validated and optimism corrected using bootstrapping techniques. We derived a point-based risk score from the regression coefficients. Calibration and discrimination were assessed by the calibration slope and C statistic. 805 participants were included in the analysis. During 1-year of follow-up, 158 participants (20%) had died. Age, Charlson-Comorbidity-Index, number of drugs, body mass index, number of hospitalizations, Barthel-Index (functional impairment), and nursing home residency were predictors of 1-year mortality in a multivariable model. Using these variables, the 1-year probability of dying could be predicted with an optimism-corrected C statistic of 0.70. The optimism-corrected calibration slope was 0.93. Based on the derived point-based risk score to predict mortality risk, 7% of the patients classified at low-risk of mortality, 19% at moderate-risk, and 37% at high-risk died after one year of follow-up. A simpler mortality score, without the Charlson-Comorbidity-Index and Barthel-Index, showed reduced discriminative power (optimism-corrected C statistic: 0.59) compared to the full score. We developed and internally validated a mortality risk index including for the first-time specific predictors for multimorbid adults. This new 1-year mortality prediction point-based score allowed to classify multimorbid older patients into three categories of increasing risk of mortality. Further validation of the score among various populations of multimorbid patients is needed before its implementation into practice.

What Happens to Full Mouth Extraction Patients? A Retrospective Review of Patient Mortality at an Academic Medical Center

When providers are forced to address the growing oral healthcare needs of an aging and sick population, full mouth extractions (FMEs) are often sought as a solution. The purpose of this observational study was to evaluate mortality rates, mortality timeline, and to identify associated risk factors. A single-center retrospective cohort study was conducted at the University of Cincinnati Medical Center. All patients who underwent FMEs at the Oral and Maxillofacial Surgery clinic from July 1, 2012 to December 31, 2019 due to caries or periodontal disease were included. Predictor variables recorded included a medical history, social history, and patient demographics. The main outcome variable was post-FME death, including the elapsed time from procedure to death. Deaths were identified using the National Death Index. Data were analyzed using simple descriptive statistics and Cox proportional hazard models. Deceased FME patients were compared to living FME patients to identify potential risk factors. Mortality risk index was derived from multivariable logistic regression. One thousand eight hundred twenty nine patients were included in the study. Nine hundred seventy six were female with a median age of 49 years (interquartile range 38-58). One thousand seven hundred nine were diagnosed with more than 1 comorbidity and 89% were on medicaid or medicare insurance. One hundred seventy patients (9.3%) were identified as deceased as of December 31, 2019. Of those who died, 87 patients were deceased within 2years of the procedure and 147 within 5years of the procedure. Statistically significant factors associated with mortality (P value < .01) included age (hazards ratio [HR] 1.01, 95% confidence interval [CI] 1.01-1.03), ASA score >3 (HR 3.12, 95% CI 2.2-4.42), nursing home residence (HR 2.66, 95% 1.67-4.28), hepatic disease (HR 1.81, 95% CI 1.18-2.78), and oncologic disease (HR 1.91, 95% 1.32-2.77). Approximately 1 in 10 patients died within 5years of FME at our center. These patients may be medically and socially compromised. More research is needed to develop FME-specific mortality indices, which may serve useful for clinical decision-making and surgical palliative care.

A flexible special case of the CSN for spatial modeling and prediction

We introduce a parsimonious, flexible subclass of the closed-skew normal (CSN) distribution that produces valid stationary spatial models. We derive and prove some relevant properties for this subfamily; in particular, we show that it is identifiable, closed under marginalization and conditioning and that a null correlation implies independence. Based on the subclass, we propose a discrete spatial model and its continuous version. We discuss why these random fields constitute valid models, and additionally, we discuss least-squares estimators for the models under the subclass. We propose to perform predictions on the model using the profile predictive likelihood; we discuss how to construct prediction regions and intervals. To compare the model against its Gaussian counterpart and show that the numerical likelihood estimators are well-behaved, we present a simulation study. Finally, we use the model to study a heuristic COVID-19 mortality risk index; we evaluate the model’s performance through 10-fold cross-validation. The risk index model is compared with a baseline Gaussian model.

A combined fracture and mortality risk index useful for treatment stratification in hip fragility fractures.

Objectives In this study, we aimed to assess the stratification ability of the Fracture and Mortality Risk Evaluation (FAME) index for reoperation, new fragility fracture, and mortality during one-year follow-up.Patients and methods Between November 2018 and July 2019, a total of 94 consecutive hip fragility fracture patients from two centers (20 males, 74 females; mean age: 79.3±8.9 years; range, 57 to 100 years) were retrospectively analyzed. The patients were classified into high, intermediate, and low fracture and mortality risk groups according to the Fracture Risk Assessment Tool (FRAX) score and Sernbo score, respectively, as well as nine combined categories according to the FAME index. Hospital records were reviewed to identify re-fractures (reoperations, implant failure, new fragility fractures on any site) and mortality at one year following the FAME index classification.Results Overall re-fracture and mortality rates were 20.2% and 33%, respectively. High fracture risk category (FRAX-H) was significantly associated with higher re-fracture (odds ratio [OR]: 2.9, 95% confidence interval [CI]: 1-8.2, p=0.037) and mortality rates compared to others (OR: 3.7, 95% CI: 1.5-9.3, p=0.003). The patients classified within the FRAX-H category (n=35) had different mortality rates according to their Sernbo classification; i.e., patients classified as low mortality risk (Sernbo-L) (n=17) had lower mortality rates compared to others in this group (n=18) (35.3% and 66.7%, respectively), indicating a low statistical significance (OR: 0.3, 95% CI: 0.1-1.1, p=0.063). Similarly, within patients classified in Sernbo-L category (n=64), those classified as high fracture risk (FRAX-H) (n=17) had significantly higher re-fracture rates compared to others in this group (n=47) (35.3% and 8.5%, respectively), (OR: 5.9; 95% CI: 1.4-24.5), (p=0.017). Multivariate logistic regression analyses adjusting for covariates (age, sex, length of hospital stay and BMI) yielded similar results.Conclusion The FAME index appears to be a useful stratification tool for allocating patients in a randomized-controlled trial for augmentation of hip fragility fractures.

The COVID-19 Assessment for Survival at Admission (CASA) Index: A 12 Months Observational Study.

Objective: Coronavirus disease 2019 (COVID-19) is a disease with a high rate of progression to critical illness. However, the stratification of patients at risk of mortality is not well defined. In this study, we aimed to define a mortality risk index to allocate patients to the appropriate intensity of care.Methods: This is a 12 months observational longitudinal study designed to develop and validate a pragmatic mortality risk score to stratify COVID-19 patients aged ≥18 years and admitted to hospital between March 2020 and March 2021. Main outcome was in-hospital mortality.Results: 244 patients were included in the study (mortality rate 29.9%). The Covid-19 Assessment for Survival at Admission (CASA) index included seven variables readily available at admission: respiratory rate, troponin, albumin, CKD-EPI, white blood cell count, D-dimer, Pa02/Fi02. The CASA index showed high discrimination for mortality with an AUC of 0.91 (sensitivity 98.6%; specificity 69%) and a better performance compared to SOFA (AUC = 0.76), age (AUC = 0.76) and 4C mortality (AUC = 0.82). The cut-off identified (11.994) for CASA index showed a negative predictive value of 99.16% and a positive predictive value of 57.58%.Conclusions: A quick and readily available index has been identified to help clinicians stratify COVID-19 patients according to the appropriate intensity of care and minimize hospital admission to patients at high risk of mortality.

What's New in Shock, July 2021?

What's New in Shock, July 2021?

Proactive Palliative Care for Hospitalized Primary Care Patients (QI712)

1.Discuss whether screening with a mortality risk index (Levine mortality risk score) can identify primary care patients with unmet palliative care needs.2.Consider the potential impact of proactive palliative care consultation on advanced care planning. Early integration of palliative care (PC) improves outcomes for patients with cancer, including quality of life, symptom burden, and health care utilization. Strategies are needed to identify primary care patients who would benefit from early PC. This project aims to: 1) determine whether the Levine mortality risk score identifies hospitalized primary care patients with unmet PC needs; 2) determine the acceptability of pro-active PC consultation to inpatient physicians and 3) explore the impact of pro-active PC on advance care planning (ACP) documentation. At hospitalization, primary care practice patients are screened with the Levine mortality risk score. An e-mail (‘trigger’) is sent to the inpatient attending for patients with a high mortality risk score offering proactive PC consultation. Patients who receive proactive PC consultation (pro-PC) are compared to patients whose attending physicians decline PC (pro-PC declined), as well as the high-mortality risk patients who were not ‘triggered’ (control). We compare rates of documented HCP, code status, ACP, length of stay, discharge location, and hospice utilization among the 3 arms. Over 37 days, 58 inpatients met criteria for high-mortality risk. Twenty-seven (47%) patients were ‘triggered’. Eleven of twenty-seven (41%) accepted proactive PC. HCP documentation rates were high at admission (73% pro-PC, 94% pro-PC declined, 100% control) and discharge (100%, 94%, 100%). The rate of ACP documentation increased in the pro-PC group (9% to 73%; p =0.003), but not the pro-PC declined (13% to 31%; p =0.20) or control (16% to 32%; p = 0.14). Patients from a primary care practice with a high mortality risk at the time of hospitalization have unmet palliative care needs, particularly in the domain of ACP. Nearly half of inpatient physicians accepted pro-PC, which was associated with higher rates of ACP at discharge.

Survival after palliative radiation therapy for cancer: The METSSS model

BackgroundWe propose a predictive model that identifies patients at greatest risk of death after palliative radiotherapy, and subsequently, can help medical professionals choose treatments that better align with patient choice and prognosis. MethodsThe National Cancer Database was queried for recipients of palliative radiotherapy during first course of treatment. Cox regression models and adjusted hazard ratios with 95% confidence intervals were used to evaluate survival predictors. The mortality risk index was calculated using predictors from the estimated Cox regression model, with higher values indicating higher mortality risk. Based on tertile cutpoints, patients were divided into low, medium, and high risk groups. ResultsA total of 68,505 patients were included from 2010-2014, median age 65.7 years. Several risk factors were found to predict survival: (1) location of metastases (liver, bone, lung, and brain); (2) age; (3) tumor primary (prostate, breast, lung, other); (4) gender; (5) Charlson-Deyo comorbidity score; and (6) radiotherapy site. The median survival times were 11.66 months, 5.09 months, and 3.28 months in the low (n=22,621), medium (n=22,638), and high risk groups (n=22,611), respectively. A nomogram was created and validated to predict survival, available online, https://tinyurl.com/METSSSmodel. Harrel’s C-index was 0.71 and receiver operator characteristic area under the curve was 0.76 at 4 years. ConclusionWe created a predictive nomogram for survival of patients receiving palliative radiotherapy during their first course of treatment (named METSSS), based on Metastases location, Elderly (age), Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy.

Association of the erythropoiesis-stimulating agent resistance index and the geriatric nutritional risk index with cardiovascular mortality in maintenance hemodialysis patients.

Hyporesponsiveness to erythropoiesis-stimulating agent (ESA) may be associated with protein-energy wasting. We investigated the relationship of the ESA resistance index (ERI) and the geriatric nutritional risk index (GNRI) for cardiovascular mortality in hemodialysis (HD) patients. A total of 180 maintenance HD patients were enrolled. The patients were stratified by the GNRI of 91.2, a previously reported cut-off value, and the ERI of 13.7 (IU/week/kg/g/dL), a cut-off value for predicting cardiovascular-specific mortality, and they were classified into four groups (group 1[G1]: higher GNRI and lower ERI, G2: higher GNRI and higher ERI, G3: lower GNRI and lower ERI, G4: lower GNRI and higher ERI). The ERI was independently associated with the GNRI (β = -0.271, p = 0.0005). During a median follow-up of 4.6 years, higher ERI and lower GNRI were independently associated with cardiovascular mortality, respectively (adjusted hazard ratio [aHR], 3.10; 95% confidence interval [CI], 1.31-7.34, and aHR, 6.64; 95%CI, 2.60-16.93, respectively). The 7-year survival rates were 96.1%, 70.3%, 77.3%, and 50.1% in G1, G2, G3, and G4, respectively. The aHR values for G4 versus G1 were 12.63 (95%CI, 3.58-44.59). With regards to model discrimination, adding the GNRI alone, the ERI alone, and both to the traditional risk model significantly improved the net reclassification improvement by 0.421, 0.662, and 0.671, respectively. Similar results were obtained for all-cause mortality. The ERI was independently associated with the GNRI, and could predict cardiovascular mortality in HD patients. Moreover, the combination of GNRI and ERI could improve the predictability for cardiovascular mortality.

Merging Pediatric Index of Mortality (a physiologic instability measure), lactate, and Systemic Inflammation Mortality Risk to better predict outcome in pediatric sepsis

Merging Pediatric Index of Mortality (a physiologic instability measure), lactate, and Systemic Inflammation Mortality Risk to better predict outcome in pediatric sepsis

Primary Care Prognostic (PCP) Index of 11-Year Mortality Risk: Development and Validation of a Brief Prognostic Tool.

Healthcare providers use a life expectancy of at least 5 to 10 years in shared clinical decision-making with older adults about cancer screening, major surgeries, and disease prevention interventions. At present, few prognostic indexes predict long-term mortality beyond 10 years or are suited for use in primary care settings. We developed and validated an 8-item multidimensional index predicting 11-year mortality for use in primary care. Using data from the Singapore Longitudinal Ageing Studies (SLAS), we developed a Primary Care Prognostic (PCP) Index for predicting 11-year mortality risk in a development cohort (n = 1550) and validated it in a geographically different cohort (n = 928). The PCP Index was derived from eight indicators (body mass loss, weakness, slow gait, comorbidity, polypharmacy, IADL/BADL dependency, low albumin, low total cholesterol, out of 25 candidate indicators) using stepwise Cox proportional hazard models. In the developmental cohort, the mortality hazard ratio increased by 53% per PCP point score increase, independent of age and sex. Across risk categories, absolute risks of mortality increased from 5% (score 0) to 67.9% (scores 7-9), with area under curve (AUC = 0.77 (95% CI 0.73-0.80)). The PCP Index also predicted mortality in the validation cohort, with AUC = 0.70 (95% CI 0.64-0.75). The PCP Index using simple clinical assessments and point scoring is a potentially useful prognostic tool for predicting long-term mortality and is well suited for risk stratification and shared clinical decision-making with older adults in primary care.

ACP Risk Grade: A Simple Mortality Index for Patients with Confirmed or Suspected Severe Acute Respiratory Syndrome Coronavirus 2 Disease (COVID-19) During the Early Stage of Outbreak in Wuhan, China

Background: Since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) outbreaks in Wuhan, China, healthcare systems capacities in highly endemic areas have been overwhelmed. Approaches to efficient management are urgently needed and key to a quicker control of the outbreaks and casualties. We aimed to characterize the clinical features of hospitalized patients with confirmed or suspected COVID-19, and develop a mortality risk index for COVID-19 patients. Methods: In this retrospective one-centre cohort study, we included all the confirmed or suspected COVID-19 patients hospitalized in a COVID-19-designated hospital from January 21 to February 5, 2020. Demographic, clinical, laboratory, radiological and clinical outcome data were collected from the hospital information system, nursing records and laboratory reports. Findings: Of 577 patients with at least one post-admission evaluation, the median age was 55 years (interquartile range [IQR], 39 - 66); 254 (44·0%) were men; 22·8% (100/438) were severe pneumonia on admission, and 37·7% (75/199) patients were SARS-CoV-2 positive. The clinical, laboratory and radiological data were comparable between positive and negative SARS-CoV-2 patients. During a median follow-up of 8·4 days (IQR, 5·8 - 12·0), 39 patients died with a 12-day cumulative mortality of 8·7% (95% CI, 5·9% to 11·5%). A simple mortality risk index (called ACP index), composed of Age and C-reactive Protein, was developed. By applying the ACP index, patients were categorized into three grades. The 12-day cumulative mortality in grade three (age ≥ 60 years and CRP ≥ 34 mg/L) was 33·2% (95% CI, 19·8% to 44·3%), which was significantly higher than those of grade two (age ≥ 60 years and CRP < 34 mg/L; age < 60 years and CRP ≥ 34 mg/L; 5·6% [95% CI, 0 to 11·3%]) and grade one (age < 60 years and CRP < 34 mg/L, 0%) (P <0·001), respectively. Interpretation: The ACP index can predict COVID-19 related short-term mortality, which may be a useful and convenient tool for quickly establishing a COVID-19 hierarchical management system that can greatly reduce the medical burden and therefore mortality in highly endemic areas. Funding Statement: Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program. Declaration of Interests: The authors stated: None reported. Ethics Approval Statement: The study was conducted in accordance with the guidelines of the Declaration of Helsinki and the principles of good clinical practice, and was approved by the Nanfang Hospital Ethics Committee (NFEC-2020-026).